Abstract
The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 37th International Conference on Machine Learning |
| Publisher | MLResearch Press |
| Number of pages | 2 |
| Publication status | Published - 2020 |
| Event | Thirty-seventh International Conference on Machine Learning: ICML2020 - Duration: 13 Jul 2020 → 18 Jul 2020 |
Publication series
| Name | Proceedings of Machine Learning Research |
|---|---|
| Publisher | MLResearch Press |
| Volume | 108 |
| ISSN (Electronic) | 2640-3498 |
Conference
| Conference | Thirty-seventh International Conference on Machine Learning |
|---|---|
| Period | 13/07/20 → 18/07/20 |
Fingerprint
Dive into the research topics of 'Impact of Legal Requirements on Explainability in Machine Learning'. Together they form a unique fingerprint.Student theses
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Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction
Author: Bibal, A., 16 Nov 2020Supervisor: FRENAY, B. (Supervisor), VANHOOF, W. (President), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (External person) (Jury) & Galarraga, L. A. (External person) (Jury)
Student thesis: Doc types › Doctor of Sciences
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